Update README.md

README.md

@@ -15,7 +15,7 @@ You use this model as you would any other XLM-RoBERTa model, taking into account
 ```
 from transformers import AutoTokenizer, XLMRobertaModel
 
-tokenizer = AutoTokenizer.from_pretrained("xlm-roberta-large")
+tokenizer = AutoTokenizer.from_pretrained("facebook/MEXMA")
 model = XLMRobertaModel.from_pretrained("facebook/MEXMA", add_pooling_layer=False)
 example_sentences = ['Sentence1', 'Sentence2']
 example_inputs = tokenizer(example_sentences, return_tensors='pt')
@@ -25,6 +25,15 @@ sentence_representation = outputs.last_hidden_state[:, 0]
 print(sentence_representation.shape) # torch.Size([2, 1024])
 ```
 
+You can also use this model with SentenceTransformers:
+```
+from sentence_transformers import SentenceTransformer
+model = SentenceTransformer("facebook/MEXMA")
+example_sentences = ['Sentence1', 'Sentence2']
+sentence_representation = model.encode(example_sentences)
+print(sentence_representation.shape) # torch.Size([2, 1024])
+```
+
 # License
 This model is released under the MIT license.